如何用R从Oanda获得3年的历史价格系列？

Question

我想在R中处理比特币价格,但我无法从雅虎和谷歌下载时间系列.

来自雅虎的BTCUSD历史时间系列缺失,谷歌无法识别getSymbols符号为"CURRENCY:EURUSD"时形成的URL .我知道R期望":"成为一个列表,所以我应用了我在Stakeoverflow中找到的解决方法来转换CURRENCY:CURRENCY.EURUSD中的EURUSD,但Google仍然无法处理请求.

从Oanda下载的工作就像一个魅力,但要求不能超过500天.我尝试这种解决方法绕过限制,但它无法正确填充prices我有其他符号的对象:

• 出于某种原因,2012年和2013年的部分时间缺少BTCUSD价格
• 还有符号列表中的符号与wo一起获得NA.

tail(prices) (带有波纹管)

             UUP    FXB    FXE    FXF   FXY   SLV    GLD     BTC
2014-08-31    NA     NA     NA     NA    NA    NA     NA 506.809
2014-09-30 22.87 159.33 124.48 102.26 88.80 16.35 116.21 375.386
2014-10-31 23.09 157.20 123.49 101.45 86.65 15.50 112.66 341.852
2014-11-30    NA     NA     NA     NA    NA    NA     NA 378.690
2014-12-31 23.97 153.06 119.14  98.16 81.21 15.06 113.58 312.642
2015-01-24    NA     NA     NA     NA    NA    NA     NA 229.813

提取物print(prices)(带有波纹管)

2013-06-28 22.56 150.17 128.93 103.92 98.63 18.97 119.11      NA
2013-07-31 22.09 150.12 131.74 105.99 99.93 19.14 127.96      NA
2013-08-30 22.19 152.93 130.84 105.45 99.63 22.60 134.62      NA
2013-09-30 21.63 159.70 133.85 108.44 99.47 20.90 128.18 133.794
2013-10-31 21.63 158.10 134.29 108.03 99.38 21.10 127.74 203.849
2013-11-30    NA     NA     NA     NA    NA    NA     NA 1084.800
2013-12-31 21.52 163.30 135.99 109.82 92.76 18.71 116.12 758.526
2014-01-31 21.83 161.95 133.29 108.00 95.58 18.45 120.09 812.097

tail(prices) (没有波纹管)

             UUP    FXB    FXE    FXF   FXY   SLV    GLD
2014-08-29 22.02 163.23 129.54 106.42 93.61 18.71 123.86
2014-09-30 22.87 159.33 124.48 102.26 88.80 16.35 116.21
2014-10-31 23.09 157.20 123.49 101.45 86.65 15.50 112.66
2014-11-28 23.47 153.46 122.46 101.00 82.01 14.83 112.11
2014-12-31 23.97 153.06 119.14  98.16 81.21 15.06 113.58
2015-01-23 25.21 147.23 110.33 110.95 82.57 17.51 124.23

这段代码有什么问题？Tx!

require(quantmod)
require(PerformanceAnalytics)

symbols <- c(
  "UUP",
  "FXB",
  "FXE",
  "FXF",
  "FXY",
  "SLV",
  "GLD"
)

getSymbols(symbols, from="2004-01-01")

prices <- list()
for(i in 1:length(symbols)) {
  prices[[i]] <- Cl(get(symbols[i]))  
}

BTC <- list()
for(i in 1:2) {
  BTC[[1]] <- getFX("BTC/USD",
                       from = Sys.Date() -499 * (i + 1),
                       to = Sys.Date() - 499 * i,
                       env = parent.frame(),
                       auto.assign = FALSE)
}
BTC[[1]] <- getFX("BTC/USD",
                  from = Sys.Date() -499,
                  to = Sys.Date(),
                  env = parent.frame(),
                  auto.assign = FALSE)

prices[[length(symbols)+1]] <- BTC[[1]]
prices <- do.call(cbind, prices)

colnames(prices) <- gsub("\\.[A-z]*", "", colnames(prices))
ep <- endpoints(prices, "months")

prices <- prices[ep,]
prices <- prices["1997-03::"]

Answer 1

你的for循环没有使用i,然后在for循环后你覆盖结果(列表的长度为1,因为它BTC[[1]]是硬编码的)

试试这个

btc <- do.call(rbind, lapply(0:2, function(i) {
  getFX("BTC/USD", 
        from = Sys.Date() -499 * (i + 1),
        to = Sys.Date() - 499 * i,
        env=NULL)
}))

prices <- do.call(cbind, c(prices, list(btc)))

编辑:这是一个更完整的例子

library(quantmod)
# Use tryCatch() in case we try to get data too far in the past that 
# Oanda doesn't provide. Return NULL if there is an error, and Filter
# to only include data that has at least 1 row.
btc <- do.call(rbind, Filter(NROW, lapply(0:5, function(i) {
  tryCatch(getFX("BTC/USD", 
                 from = Sys.Date() -499 * (i + 1),
                 to = Sys.Date() - 499 * i,
                 env=NULL), error=function(e) NULL)
})))

symbols <- c(
  "UUP",
  "FXB",
  "FXE",
  "FXF",
  "FXY",
  "SLV",
  "GLD"
)
e <- new.env()
getSymbols(symbols, from=start(btc), env=e)
prices <- do.call(cbind, c(eapply(e, Cl)[symbols], list(btc)))
colnames(prices) <- gsub("\\.[A-z]*", "", colnames(prices))
head(na.locf(prices)[endpoints(prices, "months")])
#             UUP    FXB    FXE    FXF    FXY   SLV    GLD     BTC
#2010-07-31 23.74 156.15 129.88  95.38 114.60 17.58 115.49 0.06386
#2010-08-31 24.12 152.60 126.25  97.80 117.83 18.93 122.08 0.06441
#2010-09-30 22.84 156.33 135.81 101.00 118.57 21.31 127.91 0.06194
#2010-10-31 22.37 159.45 138.69 100.81 122.93 24.17 132.62 0.18530
#2010-11-30 23.50 154.72 129.30  98.87 118.16 27.44 135.42 0.27380
#2010-12-31 22.71 155.77 133.09 106.25 121.75 30.18 138.72 0.29190